Exploratory Analysis

Tidy data:

• Create new poverty level variable
• Reorganize race variable

plots_df = read_csv("./data/final_regression_variables_clean.csv") %>%
    mutate(poverty_level = cut(poverty, breaks = c(-Inf, 10, 20, 30, 40, Inf), labels = c("poverty_10","poverty_20", "poverty_30", "poverty_40", "poverty_40+"))) %>% 
  pivot_longer(
   cols = hispanic:other_race,
   names_to = "race",
   values_to = "percent_pop",
   values_drop_na = TRUE
 ) 

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   nta_name = col_character(),
##   nta_code = col_character()
## )

## See spec(...) for full column specifications.

Outcome: SMM

poverty Vs SMM

poverty_smm_ggplot = 
  plots_df %>% 
  ggplot(aes(x = poverty, y = smm), group = nta_name) +
  geom_point(color = "red") + 
  labs(
    title = "Exploration of Poverty and Maternal Morbidity in NYC Neighborhoods",
    x = "Percentage Population Below Federal Poverty Level",
    y = "Rate of SMM per 10,000 Deliveries")

ggplotly(poverty_smm_ggplot)

poverty2_smm_ggplot = 
  plots_df %>% 
  ggplot(aes(x = poverty_level, y = smm)) + 
  geom_boxplot() + 
  labs(
    title = "Exploration of Levels of Poverty and Maternal Morbidity in NYC Neighborhoods",
    x = "Grouped Percentage Population Below Federal Poverty Level",
    y = "Rate of SMM per 10,000 Deliveries")

ggplotly(poverty2_smm_ggplot)

## Warning: Removed 25 rows containing non-finite values (stat_boxplot).

late or no prenatal care vs SMM

prenatal_care_ggplot = 
  plots_df %>% 
  ggplot(aes(x = late_no_prenatal_care, y = smm, group = nta_name)) + 
  geom_point(color = "red") + 
  labs(
    title = "Exploration of Access to Prenatal Care and Maternal Morbidity in NYC Neighborhoods",
    x = "Percent Live Births Recieving Late or No Prenatal Care",
    y = "Rate of SMM per 10,000 Deliveries")

ggplotly(prenatal_care_ggplot)

## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

Outcome: Gonorrhea

gonorrhea vs health insurance

gonorrhea1_ggplot = 
  plots_df %>% 
  ggplot(aes(x = health_ins, y = gonorrhea), group = nta_name) + 
  geom_point(color = "green") + 
  labs(
    title = "Exploration of Health Insurance and  Gonorrhea in NYC Neighborhoods",
    x = "Percent Population with Health Insurance",
    y = "Rate of gonorrhea cases per 100,000 (2014-2015)")

ggplotly(gonorrhea1_ggplot)

gonorrhea vs medicaid

gonorrhea2_ggplot = 
  plots_df %>% 
  ggplot(aes(x = medicaid_enroll, y = gonorrhea), group = nta_name) +
  geom_point(color = "green") + 
  labs(
    title = "Exploration of Medicaid Enrollment and  Gonorrhea in NYC Neighborhoods",
    x = "Percent Population Enrolled in Medicaid",
    y = "Rate of gonorrhea cases per 100,000 (2014-2015)")

ggplotly(gonorrhea2_ggplot)

gonorrhea vs education level

gonorrhea3_ggplot = 
  plots_df %>% 
  ggplot(aes(x = edu_less_than_hs, y = gonorrhea), group = nta_name) +
  geom_point(color = "green") + 
  labs(
    title = "Exploration of Education Level and  Gonorrhea in NYC Neighborhoods",
    x = "Percent Population with Less than High School Education",
    y = "rate of gonorrhea cases per 100,000 (2014-2015)")


ggplotly(gonorrhea3_ggplot)

Outcome: Preterm Births

health insurance vs preterm birth

preterm_ggplot = 
  plots_df %>% 
  ggplot(aes(x = health_ins, y = preterm_births), group = nta_name) +
  geom_point(color = "blue") + 
  labs(
    title = "Exploration of Health Insurance and  Preterm Births in NYC Neighborhoods",
    x = "Percent Population with Health Insurance",
    y = "Percent Preterm Births Among All Live Births")

ggplotly(preterm_ggplot)

medicaid enrollment vs preterm birth

preterm2_ggplot = 
  plots_df %>% 
  ggplot(aes(x = medicaid_enroll, y = preterm_births, group = nta_name)) + 
  geom_point(color = "blue") + 
  labs(
    title = "Exploration of Medicaid Enrollment and  Preterm Births in NYC Neighborhoods",
    x = "Percent Population Enrolled in Medicaid",
    y = "Percent Preterm Births Among All Live Births")

ggplotly(preterm2_ggplot)

late or no prenatal care vs preterm births

prenatal_care_ggplot = 
  plots_df %>% 
  ggplot(aes(x = late_no_prenatal_care, y = preterm_births, group = nta_name)) + 
  geom_point(color = "blue") + 
  labs(
    title = "Exploration of Access to Prenatal Care and  Preterm Births in NYC Neighborhoods",
    x = "Percent Live Births Recieving Late or No Prenatal Care",
    y = "Percent Preterm Births Among All Live Births")

ggplotly(prenatal_care_ggplot)